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DEPRECATED: Function plot_precision_recall_curve is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: PrecisionRecallDisplay.from_predictions or PrecisionRecallDisplay.from_estimator. Plot Precision Recall Curve for binary classifiers. Extra keyword arguments will be passed to matplotlib’s plot. Read more in the User Guide. Deprecated since version 1.0: plot_precision_recall_curve is deprecated in 1.0 and will be removed in 1.2. Use one of the following class methods: from_predictions or from_estimator. Parameters estimatorestimator instanceFitted classifier or a fitted Pipeline in which the last estimator is a classifier. X{array-like, sparse matrix} of shape (n_samples, n_features)Input values. yarray-like of shape (n_samples,)Binary target values. sample_weightarray-like of shape (n_samples,), default=NoneSample weights. response_method{‘predict_proba’, ‘decision_function’, ‘auto’}, default=’auto’Specifies whether to use predict_proba or decision_function as the target response. If set to ‘auto’, predict_proba is tried first and if it does not exist decision_function is tried next. namestr, default=NoneName for labeling curve. If None, the name of the estimator is used. axmatplotlib axes, default=NoneAxes object to plot on. If None, a new figure and axes is created. pos_labelstr or int, default=NoneThe class considered as the positive class when computing the precision and recall metrics. By default, estimators.classes_[1] is considered as the positive class. New in version 0.24. **kwargsdictKeyword arguments to be passed to matplotlib’s plot. Returns displayPrecisionRecallDisplayObject that stores computed values. See also precision_recall_curveCompute precision-recall pairs for different probability thresholds. PrecisionRecallDisplayPrecision Recall visualization. |
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